Role Overview

We are seeking multiple Robotics Control Engineers to join our team, focusing on developing high-performance controllers for humanoid robots, including whole-body control, balance and locomotion stabilization, compliant manipulation, and other real-time control tasks. You will work closely with experts across multiple disciplines to design and implement advanced control systems that enable dynamic, stable, and adaptive robot motion. This is an opportunity to contribute to cutting-edge research and product development in next-generation robotic systems.

Key Responsibilities

- Design and develop C++ control algorithms and create their Python bindings to support locomotion and manipulation tasks for robots.

- Modeling robot models, calibrate robot kinematics, Identify system parameters.

- Design and develop state-of-the-art state estimation algorithms for humanoid robots.

- Integrate and deploy controllers and state estimators on software platforms, conduct simulations in physics-based environments, and perform experiments on real robots.

- Optimize system parameters based on control objectives or task requirements of the robotic system.

- Conduct testing, evaluation, and analysis of control performance and state estimation performance for robots operating in simulated and real-world conditions.

- Collaborate with engineers and technicians to analyze and address related issues (Simulation, Integration, and Hardware).

Required Qualifications

- Bachelor or Master in Robotics, Control Engineering, Mechatronics, or a related field.

- Solid understanding of robot dynamics, control theory, and optimization-based control (e.g., MPC, QP-based control).

- Proficiency in C++ and Python for real-time control and ROS/ROS2 environments.

- Experience with Gazebo, MuJoCo, Isaac Sim, or other physics simulators.

- Familiarity with state estimation, sensor fusion, and system identification.

- Hands-on experience with robot hardware (e.g., torque-controlled actuators, IMUs, force/torque sensors) is a strong advantage.

- Excellent problem-solving skills and the ability to work collaboratively in a multidisciplinary team

Preferred Qualifications

- Experience in controlling legged robots.

- Knowledge of humanoid locomotion, manipulation, or related robotics systems.

- Background in model predictive control (MPC), adaptive control, or learning-based control.

- Publication record or strong project portfolio in robot simulation, control, or real-world deployment.